Light-responsive bicyclic peptides
Bibliographic record
Abstract
In this paper, we describe a method for the synthesis of light-responsive (LR) bicyclic macrocycles from linear peptides composed of 20 natural amino acids. Small molecules, peptide macrocycles, and protein conjugates that reversibly turn their function on and off in response to visible light enabled the fields of photopharmacology and optochemical genetics. Bioactive LR molecules could be produced by grafting azobenzene or other LR-structures onto molecules with known biological functions (e.g., alpha-helical peptides). It is also possible to discover such LR ligands de novo by selecting compounds with a desired function-such as binding to a target-from a library of LR-compounds or a genetically-encoded (GE) library of LR-macrocycles. The bicyclic topology of ligands offers added value such as improved binding and stability when compared to monocyclic peptides, but approaches for the design of bicyclic light-responsive architectures are limited. To address this need, we developed a tridentate C2-symmetric hydroxyl amine and di-chlorobenzene containing azobenzene (HADCAz) LR-linker with two orthogonally reactive functionalities (chlorobenzyl and hydroxylamine) to convert a linear unprotected peptide into a bicyclic peptide in a one-pot, two-step reaction. This linker reversibly isomerizes from the trans to cis form upon irradiation with blue light (365 nm). The resulting bicyclic peptide contains two loops of amino acids, one of which is constrained with an azobenzene moiety that can change the conformation in response to visible light. A scalable synthetic route to the HADCAz linker allowed us to demonstrate its application in multiple synthetic bicyclic peptides with loops that contain 2-5 amino acids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".